4 Allegato 1 Where does Research funding go? The context The context is the National Operative Programme for Research and Competitiveness (PON R&C) which is funded by the European Union for Regional development. This program provides, in the period spanning , almost 4.5 billions euro for research and innovation in the so called convergence regions of Italy (Calabria, Campania, Puglia and Sicily) in an attempt to bring these regions closer to the European average in terms of economic development. The PON R&C has 3 priorities and 12 different interventions - competitive based calls for proposals - some managed by the Italian Ministry of Education, University and Research and others by Italian Ministry of Economic Development, with more than projects financed. The subjects eligible for fundings, depending on the type of intervention, are Universities, Research bodies, Small, Medium and Large enterprises, Individuals. The Managing Authority of PON R&C, through the Open Data section, intended to publish all information on financial management, controls and results of the program, ensuring transparency and data sharing. The data can be used for any purpose, personal or commercial, and without limitation, in order to better understand the use of the European Funds for projects on scientific research, competitiveness and industrial innovation in the Convergence Regions. The reuse of these data can be undertaken according to Creative Commons attribution 3.0 (CC-BY), selecting different formats for download (xls, pdf, csv) and surf by filter such as: Region, Sector, intervention description, beneficiary type, amounts, localization, etc. The goal The group has explored the network shape and properties of the firms and institutions involved in the national program supporting industrial research and development PON R&C. This seven-year program currently in its last phases of implementation has recently published the entire database of its projects in Open data format. In this case the research questions identified were the following: (a) do certain networks of subjects tend to win the majority of PONREC grants at Program level (PON R&C) and (b) at Regional level?

5 Then (c) what are the linkages between the beneficiaries with third party suppliers (consultants) within a project of the intervention? The team A diverse mix of policy analysts and network scientists included staff of the Italian Ministry of University and Research, and of the Evaluation Unit at the Department of Development policies, staff from the European Center for Living Technology (Venice, IT), network scientists from INRIA, Université de Bordeaux (FR) and University of Bologna (IT). The team has worked on this task in the course of the two days. The experience has been challenging, but in the end was able to provide some first hypotheses and insights into possible future avenues for policy-relevant analysis. Why call on Social Network Analysis (SNA)? Standard data analysis usually is able to reveal correlations or patterns between observed variables. Sometimes, however, testing variables for correlations fails at revealing higher level patterns that take place in the data. This is precisely what is expected here from SNA: identify influential actors based on different aspects involved in research programs and funding schemes. Answers to this type of questions can certainly support policy makers in decision-making processes and activity tracking. The data The entire database, that consists in more than 5460 records that represent more than 2200 Projects, is divided in three different datasets, which are updated every 2 months: 1. Plans and Beneficiaries 2. Amounts 3. Locations. In order to ensure a proper interpretation of the data it is necessary to specify that each project is identified by a Project code. Each subject which refer to a project has assigned a CUP and a Local Project Code. The Local Code Project and the CUP represent two ways to uniquely identify an intervention of public co-financing. (For example: If a beneficiary submit a research and a training project within the same call for proposal, it will receive two CUP codes and two Local Project Codes) The preliminary difficulties that have been confronted by the group in the first hours of the meeting included the establishment of a minimal common dictionary between the two sub-groups of the policy and the network analysts, the study of the database aimed at excluding the irrelevant information and

6 controlling possible errors. These complex problems have been dealt within the context of the statement and discussion of possible research questions of interest of the policy debate. The sense-making loop In fact this process is not linear as one would like to present it: the formulation of a research question to guide the analysis in itself represents a gradual and iterative process of refinement of the definition of an area of investigation that has the property of being commonly understood, relevant, and pursuable with the tools at hand. In the case of Group 2 the research question identified gravitated around the concept of persistence of coalitions of firms and research institutions formed in response to the competitive calls. The R&C program is in fact a repeated game in which public funds are awarded competitively to firms and research institutions in subsequent rounds following a set of rules which evolve over time, but that usually require firms to apply together through public-private coalitions. The questions and tasks Policy makers would like to know many things about these coalitions: are they stable over time? How do they evolve? Are they formed only in response to the monetary incentive, or do they pre-exist the opportunity offered? Do they survive after the policy measure stops being active, or if the group is not awarded the funds? After discussing these intertwined policy issues, the analysis has proceded in two directions. The first line of inquiry into the data guided by the Bordeaux team has used the broader database of projects funded by the PON R&C through the different rounds of competitive calls. With the objective of excluding projects irrelevant for the question defined, the analysis has required the filtering of the actors (nodes) involved in collaborative research projects, which graphically appeared to belong to a single biggest sub-network. This network component appeared to be separate from many small isolated nodes, which have been excluded in that they represented firms funded individually.

7 Two-mode versus one-mode The data that was provided actually only indirectly link firms and research institutions (left image: firms and institutions are in blue; projects are red). Indeed, it is because they jointly participate to a project that they do interact. As a consequence, it was necessary to compute a different network directly linking firms and institutions based on their co-participation to projects, turning the original two-mode network into this one-mode network only involving actors. The resulting network was, as expected, quite dense. Indeed partners of a project form a maximally linked subnetwork since they are all pairwise connected, by construction (right image: nodes are colored according to regions; node size is mapped on total funds for a firm or institution). Various techniques can be used to filter out some of the links and preserve the backbone structure of the network. However, the case we were faced with resisted all filtering techniques we tried. The network is, by nature, tightly connected. Inferring higher-level structure This was the network subjected to the analysis, that a procedure the Louvain algorithm was able to further partition into 13 sub-networks corresponding to agglomerations of firms and research institutions entertaining dense relations with each others, and weaker with the outside, in the course of the program implementation.

8 It should be noted that on the above graph, the size of circle for subgroups does not mean anything, also not the size of links between them. Interpreting the results Large part of the time in the second day of work was devoted to understanding better the properties and the characteristics of these sub-networks. Are they defined by territorial proximity, by sectoral affinity, by some other common characteristic, or by a mix of those? This exploration was done first by examining the identity of the main players within those groups and their placement within the network. In response to a curiosity which has been raised regarding the amount of program resources that each player commanded and its correlation with location within each group, the graphic representation was modified by increasing the size of the sphere representing each actor in proportion to the amount of money they received through the program funding. Other visual tricks were attempted to emphasize the industrial sector prevalence of each subgroup.

9 (Detailed image of subnetworks identified through the application of the Louvain algorithm. Node size maps to total funds for a firm/institution. Node color maps to regions (blue - Calabria; green - Campania; orange - Puglia; pink - other; gray - unknown). These first attempts did not support the conclusion that the persistent coalitions are industry or territorial based. However, quality of the data was not sufficient to answer these questions conclusively in the time available. In general, the first graphs provide a preliminary snapshot of sub-networks which could possibly correspond to persistent aggregates whose internal relations may be a by-product of the program intervention. To the extent that these images correspond to real world informal networks of relations and trust, these social resources could be employed in different, non policy-induced contexts, to the benefit of research capacity and system competitiveness. The second attempt was guided by Matteo Fortini who has used the dataset of firms and research institutions awarded funds per effect of one single most relevant intervention of the program (Industrial research). The analysis has been able to highlight different roles of the program beneficiaries: the recipients of the funds and their suppliers of services, indirectly benefiting from the project funds. The dataset used for this analysis has not yet published on the opendata section of The data was converted to a directed graph in which the nodes were the proponents and the third party suppliers, and the edges represented the relation subject X was a third-party supplier for subject Y in project Z. The raw data was filtered to keep only projects which were eventually approved in the tender. The resulting graph had 2281 nodes of which were beneficiaries, 1248 third-party suppliers and 131 were

10 both - and 782 edges, representing collaborations on 142 distinct projects. On average, on this intervention, there were 3 third-party suppliers every 2 proponents, and each project involved 5 to 6 collaborations. Degree distribution The degree distribution of the nodes is close to that of a scale-free network, suggesting that interactions between proponents and 3rd-party suppliers mimic a small world in which there are a few very connected nodes (hubs) and many loosely connected ones. Below a graph of all the interactions, georeferencing proponents (in red), 3rd-parties (in blue) and subjects acting both as proponents and 3rd-parties (in green). The size of a node is proportional to its degree (sum of in+out degree). Communities A first analysis was done on simple connectivity. There is one giant component with 421 nodes and 606 edges which represents the main structure of the interactions and 1696 other tiny components with less than 9 nodes, which are small groups of companies participating in one or two projects. We also applied both the infomap multilevel community detection and the Louvain algorithm. The Louvain algorithm finds smaller communities, which could help in finding groups of parties which tend to collaborate more closely. An example of one of the communities found by the Louvain algorithm is this:

11 N-th tier suppliers Another look into the data could take into account the neighborhood of a subject, to identify how its n-th tier suppliers interact. Here is such a visualization for Università Federico II di Napoli: Rings Finally, we looked into rings, which represent parties which help each other by acting in turn as the proponent or one of the 3rd-parties in different projects. Below, for example, it is represented a ring with 4 nodes of subjects who submitted 3 different projects.

12 The data has been polished and represented as a graph. This helped to understand more clearly the interactions between proponents and 3rd-party suppliers. In the future, it would be very interesting to insert into the graph how the total value of the project was split among the different parties, to understand better how the money flows (for now, only the total costs were given): the size of a node could be proportional to the amounts it has managed, rather than the simple number of in/out connections. Conclusion These intense two days of collaborative work did convince the group of the potentialities of the SNA. One by-product clearly was to refine the questions policy makers originally had, in light of what the data as able to uncover. As is often the case, much time as devoted to data curation and organization before the analysis and sense-making loop could be trigger. Members of the group plan to pursue their collaborative work and pursue this line of inquiry. The following steps required from this analysis involve: A deeper understanding and sharper definition of the sub-networks identified: are they really 13? would their number and membership change under different specifications of the model? how these subgroups are related to particular sectors or territories? An improved description of the territorial mapping of these network entities. The attempt at associating the region of residence of the firms returned too many errors to produce any significant outcome A better characterization of the predominance of specific industrial sectors within each subgroups. Other interesting point on which query the data for future analysis are the following: What is the relation between the above sub-networks and the results that they produce?

13 Can we see this relation at program, intervention, sector and project level. (ex: To achieve better results, is it more profitable to participate with different kind of subjects? Is there a dimension of the partnership, in terms of numerosity, which allow to reach better results in any given sector?) One result from this exercise is that it is necessary to polish the actual datasets in order to ensure a better reuse and analysis of the same and above all a greater transparency. It is therefore necessary to improve it with additional data add related for example to the result indicators and the accountability of expenditure and also the publication of all the consultants recruited by the beneficiaries of the projects.

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